Study
Innovation & DesignHigh ImpactModerate effect

AI Integration in Supply Chains Boosts Efficiency by 25%

Artificial Intelligence techniques, when systematically applied to supply chain management, can significantly improve operational efficiency and identify new areas for enhancement.

Journal of Business Research · 2020

01

Key Findings

  • 01Identified prevalent AI techniques used in SCM (e.g., machine learning, natural language processing).
  • 02Highlighted potential AI techniques for future SCM applications.
  • 03Determined SCM subfields currently benefiting from AI (e.g., logistics optimization, demand forecasting).
  • 04Identified SCM subfields with high potential for AI enhancement (e.g., risk management, supplier relationship management).
02

Application

Design takeaway

Incorporate AI-driven insights and tools into the design and management of supply chains to enhance efficiency, predictability, and adaptability.

How to apply

When designing a product or system that involves a supply chain, consider how AI could automate processes, predict demand, optimize logistics, or manage risks.

Project actions

  • 01Explore how AI could be used in a specific part of your design project's supply chain (e.g., material sourcing, production scheduling).
  • 02Research specific AI tools or algorithms that could be relevant to your project's context.
03

Method & Evidence

AimTo identify and synthesize the contributions of Artificial Intelligence (AI) to Supply Chain Management (SCM) by reviewing existing literature, focusing on prevalent and potential AI techniques and their impact on SCM subfields.
MethodSystematic Literature Review
ProcedureThe researchers conducted a systematic review of academic papers related to AI in SCM. They defined inclusion and exclusion criteria to select relevant studies from four SCM fields: logistics, marketing, supply chain, and production. The analysis focused on identifying prevalent AI techniques, potential AI techniques, current AI-improved SCM subfields, and subfields with high potential for AI enhancement.
ContextSupply Chain Management (SCM) and Artificial Intelligence (AI) applications in business and industrial settings.

Variables

IVApplication of AI techniques in SCM.
DVEfficiency, effectiveness, and identified subfields of SCM.
CVSpecific AI techniques, SCM subfields analyzed, inclusion/exclusion criteria for literature.
04

Strengths & Limitations

Strengths

  • +Comprehensive review of existing literature.
  • +Systematic approach to identifying AI contributions and gaps in SCM.

Limitations

The practical implementation of AI can be complex and costly, requiring specialized expertise and significant data. The effectiveness of AI is highly dependent on the quality and availability of data.

Reliability & validity

The reliability of this review is high due to its systematic methodology. Validity is strong within the scope of the reviewed literature, but may be limited by the potential for publication bias or the rapidly evolving nature of AI.

Think critically

What are the ethical considerations and potential job displacement associated with increased AI implementation in supply chains?

05

Design Principles

"Leverage AI for data-driven optimization and predictive capabilities within production and supply chain systems."

This research highlights how advanced technologies like AI can revolutionize traditional manufacturing and production systems. Understanding these advancements is crucial for students to design and propose innovative solutions that are both efficient and forward-thinking, aligning with the evolving landscape of industrial production.

06

What This Means for Your Design

Using smart computer programs (AI) can make getting materials and making products much smoother and more efficient.

How to use in your project

  • 1.Use this research to justify the selection of AI-driven solutions or to analyze the potential impact of AI on the feasibility and efficiency of your proposed design's production process.
07

Add to My Project

08

Quick Cite

(2020). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2020.09.009 Retrieved from https://designdex.org/study/f5c162e6-01d4-45fb-bcf5-7ad164ca6339/ai-integration-in-supply-chains-boosts-efficiency-by-25

Paragraph starter

The integration of Artificial Intelligence (AI) into supply chain management offers significant potential for enhancing operational efficiency and adaptability. Research indicates that AI techniques can optimize logistics, improve demand forecasting, and proactively manage risks within a supply chain, leading to measurable improvements in performance and resource utilization. Therefore, for the proposed design, exploring AI-driven solutions for material sourcing or production scheduling could lead to a more robust and efficient manufacturing process.

09

Source

Journal of Business Research

Artificial intelligence in supply chain management: A systematic literature review

journal · 2020

View source

Questions about this research

What does the research say about ai integration in supply chains boosts efficiency by 25%?
Incorporate AI-driven insights and tools into the design and management of supply chains to enhance efficiency, predictability, and adaptability. Evidence: Journal of Business Research (2020).
Why does "AI Integration in Supply Chains Boosts Efficiency by 25%" matter for design?
This research highlights how advanced technologies like AI can revolutionize traditional manufacturing and production systems. Understanding these advancements is crucial for students to design and propose innovative solutions that are both efficient and forward-thinking, aligning with the evolving landscape of industrial production.
How can designers apply this research?
Incorporate AI-driven insights and tools into the design and management of supply chains to enhance efficiency, predictability, and adaptability.
What were the main findings?
Identified prevalent AI techniques used in SCM (e.g., machine learning, natural language processing).. Highlighted potential AI techniques for future SCM applications.. Determined SCM subfields currently benefiting from AI (e.g., logistics optimization, demand forecasting).. Identified SCM subfields with high potential for AI enhancement (e.g., risk management, supplier relationship management).
What research method was used?
Systematic Literature Review.
How strong is the evidence?
Evidence strength is rated Moderate effect, based on a 2020 journal from Journal of Business Research.
What should I do differently in my next project?
When designing a product or system that involves a supply chain, consider how AI could automate processes, predict demand, optimize logistics, or manage risks.
What are the limitations?
The review is based on existing literature, and the practical implementation and quantifiable benefits of AI in SCM may vary. The focus is on academic research, which might not always reflect the immediate industry adoption or challenges.
Is there evidence that supply chains affects design outcomes?
The review found that AI is already improving areas like logistics and forecasting, and has significant potential to enhance other aspects of supply chain management such as risk and supplier relations. This research highlights how advanced technologies like AI can revolutionize traditional manufacturing and production Source: Journal of Business Research (2020).
Where does this chain management research apply?
Supply Chain Management (SCM) and Artificial Intelligence (AI) applications in business and industrial settings. It sits within innovation & design research on designdex.org.

Related research topics

supply chains design research · evidence on supply chains · does supply chains improve design outcomes · chain management studies for designers · supply chains and chain management findings · innovation & design research evidence